Controlling for Spurious Nonlinear Dependence in Connectivity Analyses

Poskanzer, Craig; Fang, Mengting; Aglinskas, Aidas; Anzellotti, Stefano

doi:10.1007/s12021-021-09540-9

Cited by 8 publications

(10 citation statements)

References 67 publications

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“…These 80 voxels served as the FFA ROI for our analyses. This procedure has additionally been reported in Fang et al (2019) and Poskanzer et al (2021).…”

Section: Hermite Polynomialsmentioning

confidence: 63%

“…This suggests that our model is capable of capturing existing nonlinear interactions, but that the magnitude of these interactions in the fMRI data we analyzed is small. Additionally, our current model has outperformed previous models attempting to capture the nonlinear interactions with the FFA within this same dataset (Poskanzer et al, 2021). In sum, the small effect size of the nonlinearities appears to be a feature of the data, and not a consequence of the chosen model.…”

Section: Potential Pitfalls In the Search For Nonlinearitiesmentioning

confidence: 71%

“…The central assumption of CompCor is that the signal extracted from the WM and CSF is uninformative, and thus by using these anatomically defined sources of nonneural signal, one can predict and remove fluctuations in the neural data that are of no interest. Although this method may not be able to perfectly distinguish the signal of interest from noise, it has been shown to effectively remove spurious signal in studies examining multivariate and nonlinear interactions (Li et al, 2019;Poskanzer et al, 2021;Power et al, 2012).…”

Section: Hermite Polynomialsmentioning

confidence: 99%

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Functional coordinates: Modeling interactions between brain regions as points in a function space

Poskanzer

Anzellotti

2022

Network Neuroscience

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Here, we propose a novel technique to investigate nonlinear interactions between brain regions that captures both the strength and type of the functional relationship. Inspired by the field of functional analysis, we propose that the relationship between activity in separate brain areas can be viewed as a point in function space, identified by coordinates along an infinite set of basis functions. Using Hermite Polynomials as bases, we estimate a subset of these values that serve as “functional coordinates,” characterizing the interaction between BOLD activity across brain areas. We provide a proof of the convergence of the estimates in the limit, and we validate the method with simulations in which the ground truth is known, additionally showing that functional coordinates detect statistical dependence even when correlations (“functional connectivity”) approach zero. We then use functional coordinates to examine neural interactions with a chosen seed region: the Fusiform Face Area (FFA). Using k-means clustering across each voxel’s functional coordinates, we illustrate that adding nonlinear basis functions allows for the discrimination of inter-regional interactions that are otherwise grouped together when using only linear dependence. Finally, we show that regions in V5 and medial occipital and temporal lobes exhibit significant nonlinear interactions with the FFA.

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“…These 80 voxels served as the FFA ROI for our analyses. This procedure has additionally been reported in Fang et al (2019) and Poskanzer et al (2021).…”

Section: Hermite Polynomialsmentioning

confidence: 63%

Section: Potential Pitfalls In the Search For Nonlinearitiesmentioning

confidence: 71%

Section: Hermite Polynomialsmentioning

confidence: 99%

See 1 more Smart Citation

Functional coordinates: Modeling interactions between brain regions as points in a function space

Poskanzer

Anzellotti

2022

Network Neuroscience

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show abstract

“…This suggests that our model is capable of capturing existing nonlinear interactions, but that the magnitude of these interactions in the fMRI data we analyzed is small. Additionally, our current model has outperformed previous models attempting to capture the nonlinear interactions with the FFA within this same dataset [36]. In sum, the small effect size of the nonlinearities appears to be a feature of the data, and not a consequence of the chosen model.…”

Section: Potential Pitfalls In the Search For Nonlinearitiesmentioning

confidence: 71%

Computational Fingerprints: Modeling Interactions Between Brain Regions as Points in a Function Space

Poskanzer

Anzellotti

2021

Preprint

Self Cite

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In this paper we propose a novel technique to investigate the nonlinear interactions between brain regions that captures both the strength and the type of the functional relationship. Inspired by the field of functional analysis, we propose that the relationship between activity in two different brain areas can be viewed as a point in function space, identified by coordinates along an infinite set of basis functions. Using Hermite Polynomials as basis functions, we estimate from fMRI data a truncated set of coordinates that serve as a "computational fingerprint," characterizing the interaction between two brain areas. We provide a proof of the convergence of the estimates in the limit, and we validate the method with simulations in which the ground truth is known, additionally showing that computational fingerprints detect statistical dependence also when correlations ("functional connectivity") is near zero. We then use computational fingerprints to examine the neural interactions with a seed region of choice: the Fusiform Face Area (FFA). Using k-means clustering across each voxel's computational fingerprint, we illustrate that the addition of the nonlinear basis functions allows for the discrimination of inter-regional interactions that are otherwise grouped together when only linear dependence is used. Finally, we show that regions in V5 and medial occipital and temporal lobes exhibit significant nonlinear interactions with the FFA.

show abstract

“…While linear models of connectivity are most commonly used to characterize regional interactions, several studies have reported evidence of significant nonlinear relationships between brain areas (Friston et al, 1994 ; Stephan et al, 2008 ; Marinazzo et al, 2011 ; Poskanzer et al, 2022 ). Despite nonlinear dynamics having been found across the brain, however, linear models remain popular due to sufficient performance and enhanced interpretability.…”

Section: The Puzzle Of Nonlinearitymentioning

confidence: 99%